scSLAM-seq reveals core features of transcription dynamics in single cells

Florian Erhard (), Marisa A. P. Baptista, Tobias Krammer, Thomas Hennig, Marius Lange, Panagiota Arampatzi, Christopher S. Jürges, Fabian J. Theis, Antoine-Emmanuel Saliba () and Lars Dölken ()
Additional contact information
Florian Erhard: Julius-Maximilians-University Würzburg
Marisa A. P. Baptista: Julius-Maximilians-University Würzburg
Tobias Krammer: Helmholtz-Center for Infection Research (HZI)
Thomas Hennig: Julius-Maximilians-University Würzburg
Marius Lange: Helmholtz Zentrum München–German Research Center for Environmental Health
Panagiota Arampatzi: University of Würzburg
Christopher S. Jürges: Julius-Maximilians-University Würzburg
Fabian J. Theis: Helmholtz Zentrum München–German Research Center for Environmental Health
Antoine-Emmanuel Saliba: Helmholtz-Center for Infection Research (HZI)
Lars Dölken: Julius-Maximilians-University Würzburg

Nature, 2019, vol. 571, issue 7765, 419-423

Abstract: Abstract Single-cell RNA sequencing (scRNA-seq) has highlighted the important role of intercellular heterogeneity in phenotype variability in both health and disease1. However, current scRNA-seq approaches provide only a snapshot of gene expression and convey little information on the true temporal dynamics and stochastic nature of transcription. A further key limitation of scRNA-seq analysis is that the RNA profile of each individual cell can be analysed only once. Here we introduce single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing (scSLAM-seq), which integrates metabolic RNA labelling2, biochemical nucleoside conversion3 and scRNA-seq to record transcriptional activity directly by differentiating between new and old RNA for thousands of genes per single cell. We use scSLAM-seq to study the onset of infection with lytic cytomegalovirus in single mouse fibroblasts. The cell-cycle state and dose of infection deduced from old RNA enable dose–response analysis based on new RNA. scSLAM-seq thereby both visualizes and explains differences in transcriptional activity at the single-cell level. Furthermore, it depicts ‘on–off’ switches and transcriptional burst kinetics in host gene expression with extensive gene-specific differences that correlate with promoter-intrinsic features (TBP–TATA-box interactions and DNA methylation). Thus, gene-specific, and not cell-specific, features explain the heterogeneity in transcriptomes between individual cells and the transcriptional response to perturbations.

Date: 2019
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DOI: 10.1038/s41586-019-1369-y

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